Salt marshes provide crucial ecological functions and services and are experiencing rapid losses and degradation under global climate changes and high-intensity human activities in coastal zone. However, mapping salt marsh distributions and compositions with high accuracy on the national or global scale remains challenging. Here, we used Sentinel-1 time-series data and knowledge-based automatic decision tree classifiers to produce a 10-m map of the salt marshes in China coastal zones. We established annual synthetic aperture radar (SAR) composite features to address seasonal variations and tidal dynamics in salt marshes and created five partitioned-region classifiers according to the climatic and environmental characteristics. In total, 1729 Sentinel-1 images in 2019 and 3418 in situ sites were included for the classification. The results showed that (1) the annual mean SAR composite is more stable and practical than the seasonal composite for salt marsh classification along the China coastal zones in terms of these features addressing variations in tidal fluctuation, climate zones, and salt marsh compositions; (2) SAR surface roughness can effectively separate salt marshes into sparse density and red phenotype salt marshes; (3) the total area of Chinese coastal salt marshes was 127,477.37 ha with an overall accuracy of 87.30%, it included 48.30% Spartina alterniflora, 29.19% Phragmites australis, 13.76% Suaeda spp. and 8.75% Scirpus spp., and more than 85% of salt marshes were distributed from Shandong Province to Zhejiang Province. This study showed the potential application of time-series Sentinel-1 SAR data, annual composite data and knowledge-based classifiers for large-scale coastal zones, and these data will be valuable for coastal ecological restoration and sustainability management.